Meaningful Endpoints for Idiopathic Pulmonary Fibrosis (IPF) Clinical Trials: Emphasis on 'Feels, Functions, Survives'. Report of a Collaborative Discussion in a Symposium with Direct Engagement from Representatives of Patients, Investigators, the National Institutes of Health, a Patient Advocacy Organization, and a Regulatory Agency.

Background: Idiopathic pulmonary fibrosis (IPF) carries significant mortality and unpredictable progression, with limited therapeutic options. Designing trials with patient-meaningful endpoints, enhancing the reliability and interpretability of results, and streamlining the regulatory approval process are of critical importance to advancing clinical care in IPF. Methods: A landmark in-person symposium in June 2023 assembled 43 participants from the US and internationally, including patients with IPF, investigators, and regulatory representatives, to discuss the immediate future of IPF clinical trial endpoints. Patient advocates were central to discussions, which evaluated endpoints according to regulatory standards and the FDA's 'feels, functions, survives' criteria. Results: Three themes emerged: 1) consensus on endpoints mirroring the lived experiences of patients with IPF; 2) consideration of replacing forced vital capacity (FVC) as the primary endpoint, potentially by composite endpoints that include 'feels, functions, survives' measures or FVC as components; 3) support for simplified, user-friendly patient-reported outcomes (PROs) as either components of primary composite endpoints or key secondary endpoints, supplemented by functional tests as secondary endpoints and novel biomarkers as supportive measures (FDA Guidance for Industry (Multiple Endpoints in Clinical Trials) available at: https://www.fda.gov/media/162416/download). Conclusions: This report, detailing the proceedings of this pivotal symposium, suggests a potential turning point in designing future IPF clinical trials more attuned to outcomes meaningful to patients, and documents the collective agreement across multidisciplinary stakeholders on the importance of anchoring IPF trial endpoints on real patient experiences-namely, how they feel, function, and survive. There is considerable optimism that clinical care in IPF will progress through trials focused on patient-centric insights, ultimately guiding transformative treatment strategies to enhance patients' quality of life and survival.

Generation of an iPSC line (AKOSi004-A) from fibroblasts of a female adult NPC1 patient, carrying the compound heterozygous mutation p.Val1023Serfs*15/p.Gly992Arg and of an iPSC line (AKOSi005-A) from a female adult control individual.

Niemann-Pick disease Type C (NPC) is a rare progressive neurodegenerative disorder with an incidence of 1:120,000 caused by mutations in the NPC1 or NPC2 gene leading to a massive cholesterol accumulation. Here, we describe the generation of induced pluripotent stem cells (iPSCs) of an affected female adult individual carrying the NPC1 mutation p.Val1023Serfs*15/p.Gly992Arg and an iPSC line from an unrelated healthy female adult control individual. Human iPSCs were derived from fibroblasts using retroviruses carrying the four reprogramming factors OCT4, SOX2, KLF4 and C-MYC. These lines provide a valuable resource for studying the pathophysiology of NPC and for pharmacological intervention.

Recent Trends in Baccalaureate-Prepared Registered Nurses in U.S. Acute Care Hospital Units, 2004-2013: A Longitudinal Study.

PURPOSE: To examine the trends in baccalaureate (bachelor of science in nursing)-prepared registered nurses (BSN RNs) in U.S. acute care hospital units and to project the growth in the number of BSN RNs by 2020. DESIGN: This is a longitudinal study using the Registered Nurse Education Indicators data (2004-2013) from the National Database of Nursing Quality Indicators. METHODS: The level of BSN RNs in each unit was operationalized as the proportion of nurses holding a baccalaureate degree or higher among all the nurses in a unit. Our sample included 12,194 unit-years from 2,126 units of six cohorts in 377 U.S. acute care hospitals. A hierarchical linear regression model was used to examine the trends in BSN RNs and to project future growth in the number of BSN RNs when controlling for hospital and unit characteristics and considering repeated measures in units over time and clustering of units within hospitals. RESULTS: The proportion of BSN RNs in U.S. acute care hospital units increased from 44% in 2004 to 57% in 2013 (a 30% increase); when combining all cohorts, this rate increased from 44% in 2009 to 51% in 2013. On average, the proportion of BSN RNs in a unit increased by 1.3% annually before 2010 and by 1.9% each year from 2010 on. The percentage of units having at least 80% of their nurses with a baccalaureate degree or higher increased from 3% in 2009 to 7% in 2013. Based on the current trends, 64% of the nurses working in a hospital unit will have a baccalaureate degree by 2020, and 22% of the units will reach the 80% goal by 2020. CONCLUSIONS: There was a significant increase in the proportion of BSN RNs in U.S. acute care hospital units over the past decade, particularly after 2010. However, given the current trends, it is unlikely that the goal of 80% nurses with a baccalaureate degree will be achieved by 2020. CLINICAL RELEVANCE: The U.S. nursing workforce is under educational transformation in order to meet the increasing healthcare needs. To help accelerate this transformation, further advocacy, commitment, and investment are needed from all healthcare stakeholders (e.g., policymakers, executives and managers of healthcare facilities, nursing schools, etc.).

Confirmatory Factor Analysis Alternative: Free, Accessible CBID Software.

New software that performs Classical and Bayesian Instrument Development (CBID) is reported that seamlessly integrates expert (content validity) and participant data (construct validity) to produce entire reliability estimates with smaller sample requirements. The free CBID software can be accessed through a website and used by clinical investigators in new instrument development. Demonstrations are presented of the three approaches using the CBID software: (a) traditional confirmatory factor analysis (CFA), (b) Bayesian CFA using flat uninformative prior, and (c) Bayesian CFA using content expert data (informative prior). Outcomes of usability testing demonstrate the need to make the user-friendly, free CBID software available to interdisciplinary researchers. CBID has the potential to be a new and expeditious method for instrument development, adding to our current measurement toolbox. This allows for the development of new instruments for measuring determinants of health in smaller diverse populations or populations of rare diseases.

Subgroup identification of early preterm birth (ePTB): informing a future prospective enrichment clinical trial design.

BACKGROUND: Despite the widely recognized association between the severity of early preterm birth (ePTB) and its related severe diseases, little is known about the potential risk factors of ePTB and the sub-population with high risk of ePTB. Moreover, motivated by a future confirmatory clinical trial to identify whether supplementing pregnant women with docosahexaenoic acid (DHA) has a different effect on the risk subgroup population or not in terms of ePTB prevalence, this study aims to identify potential risk subgroups and risk factors for ePTB, defined as babies born less than 34 weeks of gestation. METHODS: The analysis data (N = 3,994,872) were obtained from CDC and NCHS' 2014 Natality public data file. The sample was split into independent training and validation cohorts for model generation and model assessment, respectively. Logistic regression and CART models were used to examine potential ePTB risk predictors and their interactions, including mothers' age, nativity, race, Hispanic origin, marital status, education, pre-pregnancy smoking status, pre-pregnancy BMI, pre-pregnancy diabetes status, pre-pregnancy hypertension status, previous preterm birth status, infertility treatment usage status, fertility enhancing drug usage status, and delivery payment source. RESULTS: Both logistic regression models with either 14 or 10 ePTB risk factors produced the same C-index (0.646) based on the training cohort. The C-index of the logistic regression model based on 10 predictors was 0.645 for the validation cohort. Both C-indexes indicated a good discrimination and acceptable model fit. The CART model identified preterm birth history and race as the most important risk factors, and revealed that the subgroup with a preterm birth history and a race designation as Black had the highest risk for ePTB. The c-index and misclassification rate were 0.579 and 0.034 for the training cohort, and 0.578 and 0.034 for the validation cohort, respectively. CONCLUSIONS: This study revealed 14 maternal characteristic variables that reliably identified risk for ePTB through either logistic regression model and/or a CART model. Moreover, both models efficiently identify risk subgroups for further enrichment clinical trial design.

A novel method for expediting the development of patient-reported outcome measures and an evaluation across several populations.

Item response theory (IRT) models provide an appropriate alternative to the classical ordinal confirmatory factor analysis (CFA) during the development of patient-reported outcome measures (PROMs). Current literature has identified the assessment of IRT model fit as both challenging and underdeveloped (Sinharay & Johnson, 2003; Sinharay, Johnson, & Stern, 2006). This study evaluates the performance of Ordinal Bayesian Instrument Development (OBID), a Bayesian IRT model with a probit link function approach, through applications in two breast cancer-related instrument development studies. The primary focus is to investigate an appropriate method for comparing Bayesian IRT models in PROMs development. An exact Bayesian leave-one-out cross-validation (LOO-CV) approach (Vehtari & Lampinen, 2002) is implemented to assess prior selection for the item discrimination parameter in the IRT model and subject content experts' bias (in a statistical sense and not to be confused with psychometric bias as in differential item functioning) toward the estimation of item-to-domain correlations. Results support the utilization of content subject experts' information in establishing evidence for construct validity when sample size is small. However, the incorporation of subject experts' content information in the OBID approach can be sensitive to the level of expertise of the recruited experts. More stringent efforts need to be invested in the appropriate selection of subject experts to efficiently use the OBID approach and reduce potential bias during PROMs development.

Reliability and Validity of the NDNQI® Injury Falls Measure.

Although remarkable efforts have been made to improve patient fall reporting through the utilization of standardized definitions, injury falls reporting has rarely been examined. This study used an overall intra-class correlation coefficient (ICC) estimate and factor analysis to assess the reliability and validity of the National Database of Nursing Quality Indicators® (NDNQI®) falls with injury measure. Data were collected from an online Fall Injury Level Survey that was administered to 1,159 NDNQI site coordinators (39.7% response rate; 91% registered nurses [RNs]). Estimated overall ICC was .85. Exploratory factor analysis (EFA) with a Promax rotation (root mean square error of approximation [RMSEA] = 0.053) identified three latent factors: No Injury, Minor Injury, and Moderate/Major Injuries. Final confirmatory factor analysis (CFA) assessment (comparative fit index [CFI] = 0.914, Tucker Lewis Index [TLI] = 0.910, RMSEA = 0.048) confirmed an acceptable model fit. Results provided strong evidence that the NDNQI falls with injury measure is reliable and valid in supporting hospitals' fall prevention efforts and future injurious falls research.

A novel method for expediting the development of patient-reported outcome measures and an evaluation of its performance via simulation.

BACKGROUND: Developing valid and reliable patient-reported outcome measures (PROMs) is a critical step in promoting patient-centered health care, a national priority in the U.S. Small populations or rare diseases often pose difficulties in developing PROMs using traditional methods due to small samples. METHODS: To overcome the small sample size challenge while maintaining psychometric soundness, we propose an innovative Ordinal Bayesian Instrument Development (OBID) method that seamlessly integrates expert and participant data in a Bayesian item response theory (IRT) with a probit link model framework. Prior distributions obtained from expert data are imposed on the IRT model parameters and are updated with participants' data. The efficiency of OBID is evaluated by comparing its performance to classical instrument development performance using actual and simulation data. RESULTS AND DISCUSSION : The overall performance of OBID (i.e., more reliable parameter estimates, smaller mean squared errors (MSEs) and higher predictive validity) is superior to that of classical approaches when the sample size is small (e.g. less than 100 subjects). Although OBID may exhibit larger bias, it reduces the MSEs by decreasing variances. Results also closely align with recommendations in the current literature that six subject experts will be sufficient for establishing content validity evidence. However, in the presence of highly biased experts, three experts will be adequate. CONCLUSIONS: This study successfully demonstrated that the OBID approach is more efficient than the classical approach when the sample size is small. OBID promises an efficient and reliable method for researchers and clinicians in future PROMs development for small populations or rare diseases.

A Bayesian model for censored positive count data in evaluating breast cancer progression.

Basic science researchers transplant human cancer tissues from patients with ductal carcinoma in situ (DCIS) to animals and observe the progression of the disease. Successful transplants show invasion of human tissues across mammary ducts in animal fat pads and cause DCIS-like lesions in one or more ducts. In this work, we consider data from a recent publication of breast cancer research where positive counts of affected ducts may be subject to censoring. We fit the data with zero-truncated Poisson (ZTP) models with an informative prior of gamma. Due to the zero-truncation and right censoring, posterior distributions may not be conventional gamma and are estimated through Markov chain Monte Carlo and grid approximation. For each of the two cell lines, we fit a model with group-specific parameters for DCIS subtypes classified by the cell surface biomarkers, and another model with a homogeneous parameter across groups. Models are compared by the Deviance Information Criterion (DIC). For the chosen prior parameter values, Bayes estimates are comparative to the maximum likelihood estimates, and the DIC favors the simpler model in both cell lines.